1. FIELD OF THE INVENTION
The present invention relates to a pattern associative memory system which stores learning patterns and associates one pattern, which is most similar to an input pattern, out of the stored learning patterns in a process for recognizing pattern information such as voice and an image, and more particularly to a pattern associative memory system which is suitable for associating pattern information including vagueness or a pattern formed under the conditions in which vagueness is liable to cause cross talk errors.
2. DESCRIPTION OF THE PRIOR ART
As such pattern associative memory methods, there has been known a series of associative memory methods developed by T. Kohonen. In this regard see T. Kohonen, "Self-organization and associative memory" Springer-Verlag (1984), pp. 162-167. Moreover, other pattern associative memory methods developed by K. Nakano and by S. Omari, see K. Nakano, "Association-- A Model of Associative Memory", IEEE TRANSACTION ON SYSTEMS, MAN, AND CYBERNETICS, Vol. SMC-2, No. 3, Jul. 1972, pp. 380-388.
A correlation type associative memory method known as the most basic pattern associative memory method will hereunder be explained. A basic feature of the function for associating in this associative memory method is to adjust the transfer efficiency of wirings for transmitting signals in processes from an input of a pattern signal to be associated with to an output of the identified pattern signal by a correlation learning. In such a correlation type memory method, the kinds and number of patterns which may be previously learned and stored (or memorized) as learning pattern are limited. For instance, if the dimension of an input pattern to be identified is n, patterns which are capable of being learned and stored are limited to not more than n linearly independent patterns. Moreover, if there is some vagueness in an input pattern to be associated with, for instance, in the case where a part of the input pattern is lost or an error signal is mixed into the input signal to be associated with, it is known that cross talk errors are caused in an associative memory system.
There has been known an orthogonal learning type memory method as another basic associative memory method. The orthogonal learning type associative memory method is one in which a fault of the correlation type associative memory method is improved by orthogonalizing input patterns. This method is designed so that linearly independent patterns of not more than n can be learned and stored. But, the more incompleteness in the input pattern, the greater the cross talk errors will be.
As has been described above, in both the correlation type and orthogonal learning type memory methods, the auto-associative function does not work if incompleteness of the input pattern increases. In other words, there is a limit in the ability of removing noises and, therefore, in these methods the complete pattern associative memory is performed only when such incompleteness of the input pattern is low. The foregoing method has been adopted at least in one known system having a feedback loop, wherein a recollected output signal is fed back as an input signal,. But, such a feedback processing is not always effective for removing noise.
In addition to these methods, there have also been known, for instance, Hopfield and Boltzmann type associative memory methods which differ from the foregoing methods in constructions and principles. In these methods, the quantity of calculation for associative memory is too great and a associated result can possibly converge to a pattern other than the aforesaid learned and memorized patterns. For this reason, it is very difficult to design a practically useful system.
As has been mentioned above, in the conventional pattern associative memory methods, the ability of learning and storing patterns is insufficient and, hence, it is difficult to discriminate and learn similar learning patterns. In particular, they cannot perform a difficult learning, such as the learning of whole-part pattern in which one learning pattern is completely included in another. In addition, the conventional pattern associative memory methods have a poor ability of association and cannot process inputs having low output activity such as zero input. When there is incompleteness in input patterns, or in other words, a partial deficiency of input or an error mixed in an input, the conventional associative memory system causes cross talk errors. As a result, the conventional associative memory system associates a pattern other than patterns previously learned and memorized, and a severe problem normally arises in that the associating function of the system never works.
FIG. 13 shows an example of the results of an association obtained according to a most basic pattern associative memory method which is conventionally known. In this figure, x.sub.1 and x.sub.2 each represent an input element, while and y.sub.1 and y.sub.2 each represent an output element. If each of these two activity values of input signals represent either "0" or "1", there are four input patterns for (x.sub.1, x.sub.2), i.e., (0, 0), (1, 0), (0, 1) and (1, 1). Among these four input patterns, the maximum number of combinations of input patterns capable of being learned is only two, for instance, patterns (1, 0) and (0, 1). FIG. 13 shows a variation from input values indicated by inputted signals to be associated with output values after making the correlation type memory system learn these two input patterns. In addition, the results shown in this figure also indicate that the input signal shifts towards the pattern (0, 0) or (1, 1) which is not learned by the system. In other words, in the correlation type associative memory method, only a part of the input patterns to be associated is correctly converted into a learned pattern and is then outputted, while the other part thereof is converted into patterns differing from the learned patterns and is then outputted. For this reason, it is quite clear that the correlation type associative memory method performs a complete association from an incomplete input only with great difficulty.